| CAlphabet | The class Alphabet implements an alphabet and alphabet utility functions |
| CArray< T > | Template class Array implements a dense one dimensional array |
| CArray2< T > | Template class Array2 implements a dense two dimensional array |
| CArray3< T > | Template class Array3 implements a dense three dimensional array |
| CAsciiFile | A Ascii File access class |
| CAttributeFeatures | Implements attributed features, that is in the simplest case a number of (attribute, value) pairs |
| CAUCKernel | The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training |
| CAvgDiagKernelNormalizer | Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor) |
| CBinaryFile | A Binary file access class |
| CBinaryStream< T > | Memory mapped emulation via binary streams (files) |
| CBitString | String class embedding a string in a compact bit representation |
| CBrayCurtisDistance | Class Bray-Curtis distance |
| CCache< T > | Template class Cache implements a simple cache |
| CCanberraMetric | Class CanberraMetric |
| CCanberraWordDistance | Class CanberraWordDistance |
| CChebyshewMetric | Class ChebyshewMetric |
| CChi2Kernel | The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms |
| CChiSquareDistance | Class ChiSquareDistance |
| CClassifier | A generic classifier interface |
| CCombinedDotFeatures | Features that allow stacking of a number of DotFeatures |
| CCombinedFeatures | The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object |
| CCombinedKernel | The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination |
| CCommUlongStringKernel | The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers |
| CCommWordStringKernel | The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers |
| CCompressor | |
| CConstKernel | The Constant Kernel returns a constant for all elements |
| CCosineDistance | Class CosineDistance |
| CCplex | |
| CCPLEXSVM | |
| CCustomDistance | The Custom Distance allows for custom user provided distance matrices |
| CCustomKernel | The Custom Kernel allows for custom user provided kernel matrices |
| CDecompressString< ST > | Preprocessor that decompresses compressed strings |
| CDiagKernel | The Diagonal Kernel returns a constant for the diagonal and zero otherwise |
| CDiceKernelNormalizer | DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient) |
| CDistance | Class Distance |
| CDistanceKernel | The Distance kernel takes a distance as input |
| CDistanceMachine | A generic DistanceMachine interface |
| CDistribution | Base class Distribution from which all methods implementing a distribution are derived |
| CDomainAdaptationSVM | Class DomainAdaptiveSVM |
| CDomainAdaptationSVMLinear | Class DomainAdaptiveSVMLinear |
| CDotFeatures | Features that support dot products among other operations |
| CDotKernel | Template class DotKernel is the base class for kernels working on DotFeatures |
| CDummyFeatures | The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any) |
| CDynamicArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |
| CDynamicArrayPtr | Template Dynamic array class that creates an array that can be used like a list or an array |
| CDynInt< T, sz > | Integer type of dynamic size |
| CDynProg | Dynamic Programming Class |
| CEuclidianDistance | Class EuclidianDistance |
| CExplicitSpecFeatures | Features that compute the Spectrum Kernel feature space explicitly |
| CFeatures | The class Features is the base class of all feature objects |
| CFile | A File access base class |
| CFirstElementKernelNormalizer | Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e.  |
| CFixedDegreeStringKernel | The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d |
| CFKFeatures | The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models |
| CGaussianKernel | The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures |
| CGaussianMatchStringKernel | The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length |
| CGaussianShiftKernel | An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel |
| CGaussianShortRealKernel | The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features |
| CGCArray< T > | |
| CGeodesicMetric | Class GeodesicMetric |
| CGHMM | Class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM) |
| CGMNPLib | Class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP) |
| CGMNPSVM | Class GMNPSVM implements a one vs. rest MultiClass SVM |
| CGNPPLib | Class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP) |
| CGNPPSVM | Class GNPPSVM |
| CGPBTSVM | Class GPBTSVM |
| CHammingWordDistance | Class HammingWordDistance |
| CHash | Collection of Hashing Functions |
| CHashedWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
| CHashedWDFeaturesTransposed | Features that compute the Weighted Degreee Kernel feature space explicitly |
| CHierarchical | Agglomerative hierarchical single linkage clustering |
| CHistogram | Class Histogram computes a histogram over all 16bit unsigned integers in the features |
| CHistogramIntersectionKernel | The HistogramIntersection kernel operating on realvalued vectors computes the histogram intersection distance between sets of histograms. Note: the current implementation assumes positive values for the histograms, and input vectors should sum to 1 |
| CHistogramWordStringKernel | The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains |
| CHMM | Hidden Markov Model |
| CIdentityKernelNormalizer | Identity Kernel Normalization, i.e. no normalization is applied |
| CImplicitWeightedSpecFeatures | Features that compute the Weighted Spectrum Kernel feature space explicitly |
| CIndirectObject< T, P > | Array class that accesses elements indirectly via an index array |
| CIntronList | Class IntronList |
| CJensenMetric | Class JensenMetric |
| CKernel | The Kernel base class |
| CKernelDistance | The Kernel distance takes a distance as input |
| CKernelMachine | A generic KernelMachine interface |
| CKernelNormalizer | The class Kernel Normalizer defines a function to post-process kernel values |
| CKernelPerceptron | Class KernelPerceptron - currently unfinished implementation of a Kernel Perceptron |
| CKMeans | KMeans clustering, partitions the data into k (a-priori specified) clusters |
| CKNN | Class KNN, an implementation of the standard k-nearest neigbor classifier |
| CKRR | |
| CLabels | The class Labels models labels, i.e. class assignments of objects |
| CLaRank | |
| CLBPPyrDotFeatures | Implement DotFeatures for the polynomial kernel |
| CLDA | |
| CLibLinear | Class to implement LibLinear |
| CLibSVM | LibSVM |
| CLibSVMMultiClass | Class LibSVMMultiClass |
| CLibSVMOneClass | Class LibSVMOneClass |
| CLibSVR | Class LibSVR, performs support vector regression using LibSVM |
| CLinearClassifier | Class LinearClassifier is a generic interface for all kinds of linear classifiers |
| CLinearHMM | The class LinearHMM is for learning Higher Order Markov chains |
| CLinearKernel | Computes the standard linear kernel on CDotFeatures |
| CLinearStringKernel | Computes the standard linear kernel on dense char valued features |
| CList | Class List implements a doubly connected list for low-level-objects |
| CListElement | Class ListElement, defines how an element of the the list looks like |
| CLocalAlignmentStringKernel | The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences |
| CLocalityImprovedStringKernel | The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features |
| CLogPlusOne | Preprocessor LogPlusOne does what the name says, it adds one to a dense real valued vector and takes the logarithm of each component of it |
| CLPBoost | |
| CLPM | |
| CManhattanMetric | Class ManhattanMetric |
| CManhattanWordDistance | Class ManhattanWordDistance |
| CMatchWordStringKernel | The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet |
| CMath | Class which collects generic mathematical functions |
| CMemoryMappedFile< T > | Memory mapped file |
| CMinkowskiMetric | Class MinkowskiMetric |
| CMKL | Multiple Kernel Learning |
| CMKLClassification | Multiple Kernel Learning for two-class-classification |
| CMKLMultiClass | MKLMultiClass is a class for L1-norm multiclass MKL |
| CMKLOneClass | Multiple Kernel Learning for one-class-classification |
| CMKLRegression | Multiple Kernel Learning for regression |
| CMPDSVM | Class MPDSVM |
| CMultiClassSVM | Class MultiClassSVM |
| CMultitaskKernelMaskNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |
| CMultitaskKernelMaskPairNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |
| CMultitaskKernelMklNormalizer | Base-class for parameterized Kernel Normalizers |
| CMultitaskKernelNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |
| CMultitaskKernelPlifNormalizer | The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL |
| CMultitaskKernelTreeNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy |
| CNode | A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks |
| CNormDerivativeLem3 | Preprocessor NormDerivativeLem3, performs the normalization used in Lemma3 in Jaakola Hausslers Fischer Kernel paper currently not implemented |
| CNormOne | Preprocessor NormOne, normalizes vectors to have norm 1 |
| COligoStringKernel | This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004 |
| CCombinedDotFeatures::combined_feature_iterator | |
| CPCACut | |
| CPerceptron | Class Perceptron implements the standard linear (online) perceptron |
| CPerformanceMeasures | Class to implement various performance measures |
| CPlif | Class Plif |
| CPlifArray | Class PlifArray |
| CPlifBase | Class PlifBase |
| CPlifMatrix | Store plif arrays for all transitions in the model |
| CPluginEstimate | Class PluginEstimate |
| CPolyFeatures | Implement DotFeatures for the polynomial kernel |
| CPolyKernel | Computes the standard polynomial kernel on CDotFeatures |
| CPolyMatchStringKernel | The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length |
| CPolyMatchWordStringKernel | The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features |
| CPreProc | Class PreProc defines a preprocessor interface |
| CPruneVarSubMean | Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance |
| CPyramidChi2 | Pyramid Kernel over Chi2 matched histograms |
| CQPBSVMLib | Class QPBSVMLib |
| CRealDistance | Class RealDistance |
| CRealFileFeatures | The class RealFileFeatures implements a dense double-precision floating point matrix from a file |
| CRegulatoryModulesStringKernel | The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences |
| CRidgeKernelNormalizer | Normalize the kernel by adding a constant term to its diagonal. This aids kernels to become positive definite (even though they are not - often caused by numerical problems) |
| CSalzbergWordStringKernel | The SalzbergWordString kernel implements the Salzberg kernel |
| CScatterKernelNormalizer | |
| CScatterSVM | ScatterSVM - Multiclass SVM |
| CSegmentLoss | Class IntronList |
| CSerializableAsciiFile | |
| CSerializableFile | |
| CSet< T > | Template Set class |
| CSGObject | Class SGObject is the base class of all shogun objects |
| CSigmoidKernel | The standard Sigmoid kernel computed on dense real valued features |
| CSignal | Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process |
| CSignalModel | Class SignalModel |
| CSimpleDistance< ST > | Template class SimpleDistance |
| CSimpleFeatures< ST > | The class SimpleFeatures implements dense feature matrices |
| CSimpleFile< T > | Template class SimpleFile to read and write from files |
| CSimpleLocalityImprovedStringKernel | SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel |
| CSimplePreProc< ST > | Template class SimplePreProc, base class for preprocessors (cf. CPreProc) that apply to CSimpleFeatures (i.e. rectangular dense matrices) |
| CSNPFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
| CSNPStringKernel | The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length |
| CSortUlongString | Preprocessor SortUlongString, sorts the indivual strings in ascending order |
| CSortWordString | Preprocessor SortWordString, sorts the indivual strings in ascending order |
| CSparseDistance< ST > | Template class SparseDistance |
| CSparseEuclidianDistance | Class SparseEucldianDistance |
| CSparseFeatures< ST > | Template class SparseFeatures implements sparse matrices |
| CSparseKernel< ST > | Template class SparseKernel, is the base class of kernels working on sparse features |
| CSparsePolyFeatures | Implement DotFeatures for the polynomial kernel |
| CSparsePreProc< ST > | Template class SparsePreProc, base class for preprocessors (cf. CPreProc) that apply to CSparseFeatures |
| CSparseSpatialSampleStringKernel | Sparse Spatial Sample String Kernel by Pavel Kuksa <pkuksa@cs.rutgers.edu> and Vladimir Pavlovic <vladimir@cs.rutgers.edu> |
| CSpectrumMismatchRBFKernel | |
| CSpectrumRBFKernel | |
| CSqrtDiagKernelNormalizer | SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements |
| CStringDistance< ST > | Template class StringDistance |
| CStringFeatures< ST > | Template class StringFeatures implements a list of strings |
| CStringFileFeatures< ST > | File based string features |
| CStringKernel< ST > | Template class StringKernel, is the base class of all String Kernels |
| CStringPreProc< ST > | Template class StringPreProc, base class for preprocessors (cf. CPreProc) that apply to CStringFeatures (i.e. strings of variable length) |
| CSubGradientLPM | |
| CSubGradientSVM | Class SubGradientSVM |
| CSVM | A generic Support Vector Machine Interface |
| CSVMLight | |
| CSVMLightOneClass | |
| CSVMLin | Class SVMLin |
| CSVMOcas | Class SVMOcas |
| CSVMSGD | Class SVMSGD |
| CSVRLight | |
| CTanimotoDistance | Class Tanimoto coefficient |
| CTanimotoKernelNormalizer | TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ) |
| CTaxonomy | CTaxonomy is used to describe hierarchical structure between tasks |
| CTensorProductPairKernel | Computes the Tensor Product Pair Kernel (TPPK) |
| CTime | Class Time that implements a stopwatch based on either cpu time or wall clock time |
| CTOPFeatures | The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models |
| CTrainPredMaster | |
| CTrie< Trie > | |
| CTron | |
| CVarianceKernelNormalizer | VarianceKernelNormalizer divides by the ``variance'' |
| CWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
| CWDSVMOcas | Class WDSVMOcas |
| CWeightedCommWordStringKernel | The WeightedCommWordString kernel may be used to compute the weighted spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer length is weighted by some coefficient  ) from strings that have been mapped into unsigned 16bit integers |
| CWeightedDegreePositionStringKernel | The Weighted Degree Position String kernel (Weighted Degree kernel with shifts) |
| CWeightedDegreeRBFKernel | |
| CWeightedDegreeStringKernel | The Weighted Degree String kernel |
| CZeroMeanCenterKernelNormalizer | ZeroMeanCenterKernelNormalizer centers the kernel in feature space |
| DynArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |
| CExplicitSpecFeatures::explicit_spec_feature_iterator | |
| CHashedWDFeatures::hashed_wd_feature_iterator | |
| CHashedWDFeaturesTransposed::hashed_wd_transposed_feature_iterator | |
| IO | Class IO, used to do input output operations throughout shogun |
| joint_list_struct | |
| K_THREAD_PARAM< T > | |
| libqp_state_T | |
| MKLMultiClassGLPK | MKLMultiClassGLPK is a helper class for MKLMultiClass |
| MKLMultiClassGradient | MKLMultiClassGradient is a helper class for MKLMultiClass |
| MKLMultiClassOptimizationBase | MKLMultiClassOptimizationBase is a helper class for MKLMultiClass |
| Model | Class Model |
| Parallel | Class Parallel provides helper functions for multithreading |
| Parameter | |
| CPolyFeatures::poly_feature_iterator | |
| segment_loss_struct | Segment loss |
| SerializableAsciiReader00 | |
| ShogunException | Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs |
| CSimpleFeatures< ST >::simple_feature_iterator | |
| CSparseFeatures< ST >::sparse_feature_iterator | |
| CSparsePolyFeatures::sparse_poly_feature_iterator | |
| SSKDoubleFeature | |
| SSKFeatures | |
| SSKTripleFeature | |
| TParameter | |
| CSerializableFile::TSerializableReader | |
| TSGDataType | |
| TSparse< T > | |
| TSparseEntry< T > | |
| TString< T > | |
| Version | Class Version provides version information |
| CWDFeatures::wd_feature_iterator | |
| CImplicitWeightedSpecFeatures::wspec_feature_iterator | |
